Principal Full Stack AI Engineer

Job Description Position Summary Vertex is seeking   a Full-Stack AI   Engineer to   help build, extend, and operate our   A gentic AI platform end to end. In this role you will develop AI agents and the services, APIs, and interfaces around them, and   connect the platform to the systems, tools, and data sources that make AI valuable in real enterprise workflows.   The ideal candidate   is a versatile engineer who is comfortable across the stack   including   backend services and APIs, agent frameworks and orchestration, data and retrieval pipelines, and lightweight front-end experiences   with real depth   in   enterprise integration design,   MCP/A2A protocols, event-driven architectures,   and   authentication patterns .   This individual will play a key role in ensuring AI capabilities are embedded into day-to-day work at Vertex   and   not as isolated proofs of concept, but as production-ready solutions integrated into the enterprise ecosystem.   Key Responsibilities   Platform and integrations: connecting the agentic AI platform to enterprise systems, third-party tools, and business-critical services through MCP, A2A, APIs, webhooks, and event-driven patterns   Agent and service development: building agents, tools, and the backend services that support tool use, orchestration, memory, and action execution   Data and retrieval: making structured and unstructured enterprise content usable by AI workflows through ingestion, retrieval, and grounding patterns   User-facing experiences: building and extending lightweight interfaces, chat surfaces, and developer-facing SDKs and connectors that put agents in the flow of work   Reliability and governance: observability, evaluation, security reviews, and the standards that keep agentic solutions safe and supportable in a regulated environment   Build and ship AI and agentic capabilities end to end—from backend services and APIs to the interfaces and connectors users and developers interact with   Design, build, and maintain integrations between the AI/agentic platform and enterprise systems, internal applications, third-party tools, and business services   Implement secure, scalable connectivity patterns that enable AI agents and workflows to access data, trigger actions, and interact with enterprise platforms   Develop and support integrations using MCP, A2A, APIs, webhooks, and event-driven architectures   Create reusable connectors, SDKs, middleware components, and service patterns that accelerate onboarding of new tools and systems into the AI platform   Implement agent capabilities such as tool use, orchestration, retrieval, and action execution, and tune them for accuracy, latency, and cost   Partner with platform, product, data, security, and enterprise application teams to ensure solutions meet technical, business, and compliance requirements   Design robust authentication and authorization patterns for AI services, including identity propagation, token handling, and access control   Enable AI workflows to interact reliably with core enterprise platforms such as collaboration tools, knowledge systems, workflow tools, and line-of-business applications   Build data and retrieval pipelines that make structured and unstructured sources usable by AI and agentic applications   Implement monitoring, logging, tracing, evaluation, error handling, and alerting across agents and integration services   Ensure solutions are resilient, maintainable, observable, and aligned with enterprise architecture standards   Contribute to platform governance by defining standards, documentation, onboarding guidance, and best practices   Troubleshoot and resolve issues related to connectivity, data exchange, authentication, latency, and downstream service dependencies   Evaluate emerging standards and tools in AI engineering, system interoperability, and enterprise integration to inform platform evolution   Required Qualifications   Bachelor's degree in Computer Science , Information Systems, Software Engineering, or a related technical field; equivalent practical experience may be considered   Experience in software engineering, AI/application engineering, or platform engineering, including shipping production services   Full-stack development experience in Python and/or TypeScript, spanning backend services and APIs plus modern front-end or developer-facing surfaces   Hands-on experience building with LLMs and agent frameworks, including tool calling, orchestration, retrieval-augmented generation, prompting, and evaluation   Strong hands-on experience designing and implementing API-based integrations in enterprise environments   Experience with MCP, A2A, service-to-service communication, or similar integration/interoperability patterns   Strong understanding of event-driven architecture, messaging systems, asynchronous processing, and workflow-based integration design   Experience with authentication and authorization standards such as OAuth, OIDC, SSO, service principals, secrets management, and token-based security   Familiarity with enterprise application integration patterns, middleware, and distributed systems design   Experience building secure, scalable, and reliable production services on modern cloud platforms   Strong problem-solving skills and ability to work across multiple systems, teams, and technical domains   Strong written and verbal communication skills with the ability to document and explain technical approaches clearly   Understanding of AI-native software engineering practices, including effective and responsible use of AI coding assistants and software engineering agents   Technical Skills   Python and/or TypeScript across backend and front-end   LLM APIs, agent frameworks, tool calling, and orchestration   Prompt design, evaluation harnesses, and quality/cost/latency tuning   Retrieval-augmented generation, embe